As enterprise data lakes evolve into mission-critical lakehouses powering AI models and real-time business intelligence, open table formats like Apache Iceberg have become essential infrastructure. However, a key vulnerability has persisted: native Iceberg snapshots are merely metadata pointers, not full backups. If a table is corrupted by ransomware, deleted by an errant script, or overwritten by a misfiring AI agent, those pointers vanish. Restoring underlying storage files leaves organizations with raw, disorganized Parquet files requiring days of manual metadata reconstruction before analytics engines can query the data.

Rubrik’s new capability closes this vulnerability by backing up both data files and table metadata, automatically re-registering tables in the catalog upon recovery. This allows data teams to restore complete, immediately queryable tables in engines like Amazon Athena, Apache Spark, or Trino.

Also Read: Airties Debuts “Aura” Agentic AI Engine to Transform Connected Intelligence for ISPs

Technical Capabilities: Metadata Rewiring and In-Account Sovereignty

Coverage extends across AWS Glue Data Catalog and Amazon Simple Storage Service (S3) Tables. Key technical highlights include:

Iceberg-Aware Metadata Recovery: Captures full table definitions, history, and schema state alongside raw data. Upon restore, Rubrik re-wires the catalog automatically so analytics engines can resume operations without manual file repair.

In-Account Data Sovereignty: it stands out as the first Iceberg-aware protection solution that enables enterprises to keep immutable backups locked inside their own AWS accounts, satisfying data compliance and sovereignty requirements, and complementing Rubrik’s air-gapped Cloud Vault.

Petabyte-Scale Forever-Incremental Backups: makes use of forever-incremental method of compacted snapshot backups which keeps backup window low for a very large scale deployment and lets the companies select their preferred Amazon S3 storage tier to control cost.

Risk Validation Based on branch: gives ability to run backups right before the risky activities like schema changes or large batch writes by having the ability to restore data on a separate Iceberg branch for validation before promotion production environment.

Transforming the Cloud Data Security, Lakehouse, and AI Infrastructure Industry

Rubrik’s release accelerates a fundamental evolution across the broader Cloud Data Security, Data Management, and AI Infrastructure sectors.

The Obsolescence of “Storage-Only” Cloud Backups
Historically, cloud backup providers treated object storage (like Amazon S3) as simple buckets of unstructured files. However, modern analytical workloads rely on abstraction layers catalogs, schema definitions, and metadata pointers to make raw data usable.

This announcement highlights the inadequacy of storage-only backups for lakehouses. The data security market is shifting toward metadata-aware cyber resilience. Security vendors can no longer claim cloud data protection simply by backing up raw bytes; they must protect the catalog and logical table structures that give that data operational meaning.

De-Risking Autonomous AI Agents on Data Lakes
As enterprises deploy autonomous AI agents with write access to internal data lakes for automated ETL, feature engineering, and reporting, the risk of “agentic data corruption” where an AI agent incorrectly modifies schema or overwrites table states at scale has grown significantly.

By introducing Iceberg-aware recovery and isolated branch testing, Rubrik establishes a baseline safety layer for AI-driven data pipelines. Data engineering teams gain a critical safety net, allowing them to grant AI agents broader operational permissions without risking permanent lakehouse corruption.

Operational Impact on Enterprise Businesses

For top enterprise executives responsible for data lakes, mission-critical analytics, and AI on AWS – like CIOs, CISOs, and CDOs – adopting lakehouse-aware protection has numerous operational benefits:

No more Downtime and Lost Revenue
If ransomware attacks or deletions cause company data lakehouses to stop functioning, dashboards for business intelligence, real-time supply chain simulations, and consumer apps will all become inoperable. Shortening the recovery period from many days of manual reconstruction work to just a few minutes of automated catalog re-registration ensures the smooth operation of business as well as the avoidance of possible losses in revenues.

Compliance made Easy by Securing Data Sovereignty
Organizations like financial services, healthcare providers, and public institutions are at a regulatory risk when their sensitive data is moved for cloud backup as they rely on third-party backup solutions that usually require leaving data in the enterprise clouds. Granting these institutions the ability to keep their immutable and air-gapped backups on the same AWS tenant that they use will ensure full mastery over where they keep their data and how to encrypt it, even while cyber insurance and other compliance requirements get satisfied.

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Also Read: IonQ and CMC Microsystems Partner to Expand Cloud Quantum Computing Access in Canada 

“The diamond-spin approach we have applied in this prototype not only offers exceptional scalability in its own right, but also has the potential to be integrated with superconducting quantum computers to further extend their capabilities, enabling more complex and large-scale computations.” Reaffirming the long-term research partnership driving the achievement, Dr. Kees Eijkel, General Director of QuTech, added: “We are delighted to announce this prototype diamond spin quantum computer as a result of the collaborative research conducted since 2020 between Fujitsu, Delft University of Technology, and QuTech. It is a major milestone in our strong collaboration. Demonstrating the scalability expected of diamond spin quantum computing remains a long and challenging journey. However, by further strengthening our collaboration with Fujitsu, we are committed to tackling this ambitious and meaningful challenge and leading the development of next-generation quantum technologies.” Ultimately, Fujitsu plans to develop a multi-module diamond-spin prototype by 2027 as part of its broader roadmap to realize practical, fault-tolerant quantum computing by 2030.

Read More: Fujitsu develops diamond-spin quantum computer prototype

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Also Read: Guardant Health Earns FDA Approval for Guardant360 CDx

Highlighting the shift away from legacy workflows, Tim Harkness, Founder and CEO of Unchained Labs, stated: “Developability research has been stuck in the past samples moving from tool to tool, data trapped in different software and scientists left to piece together the answer. The AI Developability Workcell changes all of that. Automation, sample prep, analytics and AI work together from day one so teams can ditch the chaos, stop chasing data and start advancing the right candidates in days instead of weeks.” Ultimately, this platform-native workcell provides biopharmaceutical research organizations with an end-to-end, automated ecosystem that maximizes sample efficiency, standardizes structured data generation, and optimizes early-stage biologic drug development.

Read More: Unchained Labs Puts AI to Work, Debuts Developability Workcell

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This purchase is the next step after the partnership began in 2018. For the last eight years, the two companies have been working hand in hand to provide consumers with easy-to-access banking services through features like no-fee overdrafts, direct deposit early access, and automatic savings. With the acquisition of the national bank charter, Chime enhances its regulatory alignment, gets balance sheet flexibility, and becomes ready for long-term growth for operations.

“Since 2018, Stride Bank has been a trusted partner in our mission to unite everyday workers with financial peace of mind,” said Chris Britt, Founder and CEO of Chime. “Acquiring Stride Bank represents an extraordinary milestone for Chime. Bringing a national bank charter in-house allows us to innovate faster, expand our credit and lending products, and build an even more resilient, vertically integrated platform. We look forward to welcoming the Stride Bank team into the Chime family and continuing to deliver exceptional financial products to millions of Americans.”

Also Read: SDK.finance Launches Real-Time General Ledger for Financial Systems

Vertical Integration and Strategic Synergy

Bringing Stride Bank’s national charter, seasoned risk operations, and regulatory frameworks in-house transforms Chime’s underlying banking model. The integrated structure will streamline card issuance, clear deposit flows natively, and allow the combined entity to roll out new credit, lending, and wealth-building solutions with greater speed and cost efficiency.

Key operational benefits of the acquisition include:

Direct Charter Ownership: Secures a national bank charter to simplify regulatory oversight and expand nationwide banking capabilities.

Product Innovation Acceleration: Reduces reliance on third-party middleware, enabling faster development cycles for consumer credit and savings products.

Operational Continuity: Preserves established operational workflows while integrating Stride Bank’s specialized banking personnel into Chime’s technology organization.

Strengthened Compliance Framework: Unifies risk management, fraud prevention, and regulatory compliance under a consolidated operational architecture.

“Over the last eight years, our partnership with Chime has reshaped consumer banking in America,” said Brint Vincent, CEO of Stride Bank. “Chime’s relentless focus on member satisfaction aligns seamlessly with our heritage of banking excellence. Joining forces allows us to pool our technology and regulatory expertise to support millions of everyday workers while expanding our commitment to community banking.”

Transaction Governance and Regulatory Path

The transaction is subject to standard closing conditions, including customary regulatory approvals from federal and state banking authorities. Upon completion of the acquisition, Stride Bank will operate as a wholly owned subsidiary of Chime Financial, Inc., maintaining its operational commitments to local communities while serving as the primary infrastructure engine for Chime’s expanding member base.

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As enterprise technology environments expand in complexity, traditional IT service desks face increased friction in diagnosing endpoint issues, executing manual fixes, and scaling operational bandwidth. Tia Troubleshooting transforms this operational paradigm by evaluating device health, pinpointing root causes, applying targeted remediation scripts upon administrator approval, and verifying that the underlying problem is fully resolved.

Transitioning IT Support to Autonomous Endpoint Management

With this rollout, day-to-day IT support tasks undergo a change. From being merely a tool to guide, Tia takes charge of the incident lifecycles from one end right through to the other as a human overseer directs her. IT personnel keep their full operational control by checking the outcome of their diagnoses, giving their permission through a digital form when major system modifications are concerned or, on rare occasions, manually overwriting the system’s decisions.

Level-1 support technicians can expect to be given very structured, data-based guidance supported with previous resolution archives, so that problem solving speeds get a lift. The senior systems specialists are provided with the results of diagnostics in the background that analyze system logs and telemetry far quicker than manual methods are capable of achieving, resulting in higher first-call resolution chances for clients.

Also Read: Kroll Debuts APEX to Quantify Business Outcomes for AI Investments

Key technical capabilities of Tia Troubleshooting include:

Contextual Endpoint Diagnostics: Analyzes live device telemetry alongside an organization’s proprietary resolution history to evaluate root causes.

Dynamic Script Generation: Generates on-demand diagnostic and remediation scripts when addressing novel or complex technical errors.

Three Flexible Operating Modes: Configurable across Advise (guidance only), Diagnose (live root-cause analysis), and Resolve (end-to-end execution upon human approval).

Comprehensive Audit Logging: Automatically logs all AI-driven diagnostics, approval steps, and remediation actions within TeamViewer Session Insights for compliance and security auditing.

Granular Access Control: Enables system administrators to assign specific Tia capability tiers to individual user groups, IT roles, and device policies.

“Tia Troubleshooting shows where we believe AI in IT is heading: toward greater autonomy, but with the controls that make it practical for organizations,” said Mei Dent, Chief Product and Technology Officer at TeamViewer. “IT teams shouldn’t have to choose between doing everything manually and fully handing over control to AI. Governed autonomy offers a balanced way to expand AI’s involvement as trust develops, and that’s the direction we’re building with Tia.”

Human-in-the-Loop Governance and Workplace Autonomy

The architecture of Tia Troubleshooting is consistent with global workplace autonomy research findings that show the preference by IT management for supervised AI versus automation. The combination of automation and human supervision through the use of TeamViewer enables organizations to scale up their operations, govern themselves securely, and remove digital friction from the workforce.

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Addressing this operational bottleneck, global AI connectivity software leader Airties announced the launch of Airties Aura. Designed as a comprehensive Agentic AI engine for homes and small-to-medium businesses (SMBs), Aura moves past simple passive analytics to deliver autonomous, role-specific troubleshooting and real-time execution across the entire broadband lifecycle.

Short for Agentic AI that Understands, Reasons, and Automates, Aura powers Airties’ entire connectivity software portfolio. Operating across 50 million managed homes and billions of daily Wi-Fi telemetry points, the engine translates raw network data into closed-loop actions—ranging from automated mesh re-optimization to guided subscriber self-care.

Also Read: Cloudflare and OpenAI Partner to Redefine Vulnerability Management with AI-Powered Edge Defense

Technical Architecture: Understand, Reason, and Automate at Scale

The core innovation behind Airties Aura is its transition from passive monitoring to autonomous workflow execution. Built with strict human-in-the-loop governance controls, the engine operates on a three-stage functional loop:

Understand: Continuously senses environment health by parsing live telemetry across customer premises equipment (CPE), connected devices, extender meshes, and active applications.

Reason: Utilizes domain-grounded models to isolate root causes for example, distinguishing between a congested fiber link and a weak Wi-Fi backhaul on a specific extender node.

Automate: Executes real-time fixes autonomously (e.g., steering high-bandwidth streaming devices to 5GHz bands or re-configuring mesh paths) or surfaces role-tailored recommendations to operator teams.

Aura integrates directly into established operator platforms including Airties Careview and supports open, agent-to-agent interoperability via standardized data models. This enables ISPs to surface natural-language summaries for customer care agents, roll back unstable firmware for network operations, or deliver plain-English self-service steps inside consumer mobile apps.

Transforming the Telecommunications and Broadband Managed Services Industry

The release of an agentic AI engine for home network management reflects a broader, structural evolution across the Telecommunications, ISP Operations, and Smart Home Infrastructure landscape.

The Obsolescence of Static Diagnostic Dashboards
For over a decade, managed Wi-Fi vendors competed on the depth of their diagnostic graphs and metric dashboards. However, providing customer care agents with endless signal-to-noise charts often increased average handle times (AHT) as support representatives struggled to interpret complex RF data.

Airties Aura signals the end of passive diagnostic dashboards. The broadband industry is pivoting toward intent-based, autonomous operations. Telecommunications software is no longer evaluated strictly on data collection metrics, but on its ability to automate resolution, lower mean-time-to-repair (MTTR), and prevent support calls altogether.

Establishing “Supervised Autonomy” as an Operator Standard
Broadband operators manage mission-critical residential infrastructure and are understandably cautious regarding unmonitored AI actions that could accidentally disconnect whole neighborhoods.

By combining autonomous agent execution with role-based permissions and auditable safety boundaries, Airties models how telecommunications software can safely deploy AI at scale. Operator teams retain total oversight over population-scale interventions while allowing routine, single-home optimizations to occur automatically in the background.

Broad Operational Impact on Broadband Providers and Enterprises

For telecom executives, Chief Technology Officers (CTOs), and Vice Presidents of Customer Experience navigating rising operating expenses (OpEx) and high subscriber churn, adopting agentic network intelligence delivers immediate financial and operational advantages:

Insulating Operating Margins Against Field Service Costs
Rolling a technician truck to a subscriber’s home is one of the single most expensive operational events for an ISP. Resolving mesh bottlenecks, client steering errors, and physical placement issues autonomously through software allows broadband providers to drastically slash truck roll volumes, directly protecting operating margins.

Lowering Subscriber Churn and Enhancing Customer Lifetime Value (LTV)
In the highly competitive broadband industry, bad WiFi experience is the biggest reason for customer churn. Network optimization before the customer can feel the impact on their video streaming performance will not only boost Net Promoter Scores (NPS), but will also help to keep Average Revenue Per User (ARPU).

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Also Read: Abcam and Genedata Collaborate to Accelerate AI-Driven Antibody Development

Reaffirming the clinical impact of shifting from reactive treatment to proactive ctDNA surveillance, Helmy Eltoukhy, Guardant Health chairman and co-CEO, stated: “This approval represents a landmark shift in how we define disease progression and reinforces the transformative role of liquid biopsy in guiding therapy decisions earlier at a critical moment in a breast cancer patient’s treatment journey.” Representing the 29th global companion diagnostic indication for Guardant360 CDx, this approval reinforces Guardant’s extensive reimbursement footprint across Medicare and commercial payers covering over 300 million lives while establishing circulating tumor DNA monitoring as an essential clinical protocol in precision oncology workflows.

Read More: Guardant Health Announces Landmark FDA Approval of Guardant360^® CDx as Companion Diagnostic for AstraZeneca’s ETCAMAH (camizestrant) in Advanced Breast Cancer

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Also Read: Palo Alto Networks Acquires Console to Advance Agentic Security 

“As attackers use AI to move at machine speed, defenders need AI for security to detect and defend just as fast,” said Mike Nichols, general manager, Security, Elastic. “By bringing OpenAI GPT cyber models directly into Elastic Security, we can give customers additional cyber reasoning right where security work happens, without separate model infrastructure or integration overhead.” The company expects the technology to enhance investigation and remediation by combining specialized cyber reasoning with Elastic’s security data and workflows. This announcement takes further the overall partnership of Elastic with OpenAI that aims at providing advanced AI technologies to the enterprises. Security operations teams, then again, with this integration, their manual investigation tasks would get reduced. They could also respond faster to incidents and have more time to think of higher-value security decisions as the use of AI for evil purposes is the main source of the increased speed and complexity of attacks.

Read More: Elastic Brings OpenAI GPT Cyber Models Into Elastic Security to Help Defenders Investigate and Remediate Threats Faster

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Modern financial platforms frequently struggle with fragmented data architectures, where transaction execution, balance logic, and accounting entries are split across disconnected software systems. As organizations scale across multiple payment rails, international currencies, and varied customer segments, this structural fragmentation introduces delayed reporting, heavy manual reconciliation overhead, and persistent discrepancies between live operational data and core financial ledgers. SDK.finance General Ledger resolves this operational bottleneck by serving as a non-disruptive, real-time accounting layer overlaying existing core banking or payment platforms.

“Financial businesses need to know not only that money moved, but how every event affected every relevant account, immediately and with a complete audit trail,” said Alex Malyshev, CEO of SDK.finance. “We built SDK.finance General Ledger as a standalone accounting layer that can work with the infrastructure a company already has. It helps technical and finance teams work from the same financial truth while retaining control over their architecture, data and future development.”

Also Read: Ramp Signs Agreement with AWS to Power AI Finance Operations 

Transforming Product Events into Immutable Audit Trails

Operating via configurable accounting rules and automated mapping logic, SDK.finance General Ledger translates raw, product-specific event streams into structured debit and credit legs. The system validates that all journal entries are balanced, posts completed entries automatically, and calculates live account balances and turnovers instantaneously.

To maintain strict regulatory compliance and auditability, all posted entries are permanently immutable. Any subsequent financial corrections or adjustments are executed strictly through reversing entries, safeguarding the primary record and maintaining a transparent historical audit trail.

Key functional capabilities of the SDK.finance General Ledger include:

Automated Event Translation: Automatically converts incoming business events into balanced debit and credit entries.

Real-Time Balance & Turnover Reporting: Updates ledger accounts instantly to provide immediate financial visibility.

Flexible Chart of Accounts: Offers configurable mapping rules, accounting determinations, and customized ledger structures.

Immutable Accounting Ledger: Enforces immutability for posted records, managing edits exclusively via reversing entries.

Multi-Currency & High-Precision Asset Support: Handles fiat currencies alongside digital assets with precision tracking up to 18 decimal places.

Enterprise Security Controls: Features granular role-based access control (RBAC) paired with a fully traceable system audit log.

Flexible API & Stream Integration: Connects seamlessly into existing technology stacks using Apache Kafka or REST APIs.

Source Code Licensing: Provides complete source code access for enterprise organizations requiring deep software customization and self-hosted governance.

Versatile Deployment Across High-Volume Financial Sectors

Designed specifically for enterprise platforms that process high-volume financial events, SDK.finance General Ledger adapts across diverse commercial use cases. The module can be integrated independently alongside legacy core banking software or bundled with the broader SDK.finance Transaction Platform:

Banks & EMIs: Upgrades legacy core software with real-time, double-entry ledger capabilities.

Payment Service Providers (PSPs): Tracks complex processing fees, merchant settlements, and multi-rail payment flows.

Digital Wallets & Neobanks: Maintains live, accurate customer balances and operational accounting ledgers.

Cross-Border Payout Platforms: Manages multi-currency conversions and foreign-exchange transaction flows.

Embedded Finance & Marketplaces: Records split payments, merchant liabilities, and platform commissions automatically.

Digital Asset Operators: Accounts for fiat and cryptocurrency transactions within a unified, standardized ledger framework.

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The platform launch addresses an emerging class of operational risk brought into focus by the recent OpenAI-Hugging Face incident. During an internal security evaluation, autonomous AI agents identified unexpected configuration paths that extended beyond their designated environments, ultimately accessing external production infrastructure. The event demonstrated how fast-moving AI agents can uncover operational gaps, chain vulnerabilities together, and execute cloud actions at speeds that surpass traditional, human-dependent security operations.

As Greg Brockman, President and Co-Founder of OpenAI, recently wrote in his blog post “The Defender’s Window”: “The OpenAI-Hugging Face incident was a watershed moment for cybersecurity because it gave a peek into how the capabilities of a typical threat actor will evolve in upcoming months. I’ve spoken with many organizations over the past few weeks, and one theme is clear: they know they need to fundamentally uplevel their cybersecurity practices with unprecedented speed.”

“The Hugging Face incident was a warning shot. It showed what capable agents can already do when they go off-script, and why security teams need to prepare for similar techniques being used intentionally by attackers,” said Evan Reiser, Founder & CEO of Abnormal AI. “Our platform already detects behavioral anomalies from human and non-human identities and I’m excited for us to extend these capabilities to protect enterprise cloud environments.”

Also Read: Harness Launches AppSec Alliance to Secure Application, API, and Agent Workflows

Machine-Speed Defense Architecture for Enterprise Cloud Infrastructure

Legacy cybersecurity tools that rely primarily on static threat signatures struggle to identify non-standard, agent-driven exploits that lack historical precedent. Abnormal AI Cloud Security counters these gaps by applying continuous behavioral analysis—establishing baseline operational profiles for every human identity, service account, API key, and autonomous AI agent across an organization’s digital footprint.

Key architectural capabilities of Abnormal AI Cloud Security include:

Real-Time AI Breach Detection: Continuously evaluates behavioral signals across the cloud estate, flagging suspicious activity the moment an identity or agent strays from established operational norms.

Autonomous Incident Containment: Delivers machine-speed response workflows when threat conditions are met. Security administrators can configure the platform to automatically isolate compromised workloads, revoke API tokens, and contain affected resources based on incident severity.

OpenAI Daybreak Investigation Engine: Utilizes OpenAI Daybreak models to assist security teams during incident triage. The system correlates raw telemetry logs with behavioral context to map out incident blast radiuses and suggest remediation paths.

Bridging the Operational Gap in Agentic Security

By extending behavioral analytics natively into cloud workloads, Abnormal AI gives enterprise security operations centers (SOCs) the toolset needed to govern both human and software-driven actors. The platform allows security teams to maintain high-velocity cloud workflows while deploying automated guardrails that prevent autonomous agents from breaching operational boundaries.

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